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Scalar (mathematics)

About: Scalar (mathematics) is a research topic. Over the lifetime, 34298 publications have been published within this topic receiving 745509 citations.


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Journal ArticleDOI
TL;DR: In this paper, a class of approximating matrices as a function of a scalar parameter is presented, where the problem of optimal conditioning of these matrices under an appropriate norm is investigated and a set of computational results verifies the superiority of the new methods arising from conditioning considerations to known methods.
Abstract: Quasi-Newton methods accelerate the steepest-descent technique for function minimization by using computational history to generate a sequence of approximations to the inverse of the Hessian matrix. This paper presents a class of approximating matrices as a function of a scalar parameter. The problem of optimal conditioning of these matrices under an appropriate norm as a function of the scalar parameter is investigated. A set of computational results verifies the superiority of the new methods arising from conditioning considerations to known methods.

3,359 citations

Journal ArticleDOI
TL;DR: In this article, the three point correlation functions for primordial scalar and tensor fluctuations in single field inflationary models were computed in the slow roll limit, where the answer is given terms of the two usual slow roll parameters.
Abstract: We compute the three point correlation functions for primordial scalar and tensor fluctuations in single field inflationary models We obtain explicit expressions in the slow roll limit where the answer is given terms of the two usual slow roll parameters In a particular limit the three point functions are determined completely by the tilt of the spectrum of the two point functions We also make some remarks on the relation of this computation to dS/CFT and AdS/CFT We emphasize that (A)dS/CFT can be viewed as a statement about the wavefunction of the universe

3,153 citations

Journal ArticleDOI
TL;DR: In this paper, an efficient line-of-sight method was used to calculate the anisotropy and polarization of the cosmic microwave background for scalar and tensor modes in almost-Friedmann-Robertson-Walker models with positive spatial curvature.
Abstract: We implement the efficient line of sight method to calculate the anisotropy and polarization of the cosmic microwave background for scalar and tensor modes in almost-Friedmann-Robertson-Walker models with positive spatial curvature. We present new results for the polarization power spectra in such models.

2,752 citations

Journal Article
TL;DR: During the past few years several design algorithms have been developed for a variety of vector quantizers and the performance of these codes has been studied for speech waveforms, speech linear predictive parameter vectors, images, and several simulated random processes.
Abstract: A vector quantizer is a system for mapping a sequence of continuous or discrete vectors into a digital sequence suitable for communication over or storage in a digital channel. The goal of such a system is data compression: to reduce the bit rate so as to minimize communication channel capacity or digital storage memory requirements while maintaining the necessary fidelity of the data. The mapping for each vector may or may not have memory in the sense of depending on past actions of the coder, just as in well established scalar techniques such as PCM, which has no memory, and predictive quantization, which does. Even though information theory implies that one can always obtain better performance by coding vectors instead of scalars, scalar quantizers have remained by far the most common data compression system because of their simplicity and good performance when the communication rate is sufficiently large. In addition, relatively few design techniques have existed for vector quantizers. During the past few years several design algorithms have been developed for a variety of vector quantizers and the performance of these codes has been studied for speech waveforms, speech linear predictive parameter vectors, images, and several simulated random processes. It is the purpose of this article to survey some of these design techniques and their applications.

2,743 citations

Journal ArticleDOI
TL;DR: The superposition principle of the wave function is defined in this article, which is the fundamental principle of quantum mechanics that the system of states forms a linear manifold, in which a unitary scalar product is defined.
Abstract: It is perhaps the most fundamental principle of Quantum Mechanics that the system of states forms a linear manifold,1 in which a unitary scalar product is defined.2 The states are generally represented by wave functions3 in such a way that φ and constant multiples of φ represent the same physical state. It is possible, therefore, to normalize the wave function, i.e., to multiply it by a constant factor such that its scalar product with itself becomes 1. Then, only a constant factor of modulus 1, the so-called phase, will be left undetermined in the wave function. The linear character of the wave function is called the superposition principle. The square of the modulus of the unitary scalar product (ψ,Φ) of two normalized wave functions ψ and Φ is called the transition probability from the state ψ into Φ, or conversely. This is supposed to give the probability that an experiment performed on a system in the state Φ, to see whether or not the state is ψ, gives the result that it is ψ. If there are two or more different experiments to decide this (e.g., essentially the same experiment, performed at different times) they are all supposed to give the same result, i.e., the transition probability has an invariant physical sense.

2,694 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,666
20223,536
20211,927
20201,888
20191,670
20181,645